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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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17 pages, 6488 KB  
Article
A Spatial Analysis of the Association Between Urban Heat and Coronary Heart Disease
by Kyle Lucas, Ben Dewitt, Donald J. Biddle and Charlie H. Zhang
ISPRS Int. J. Geo-Inf. 2025, 14(9), 344; https://doi.org/10.3390/ijgi14090344 - 7 Sep 2025
Viewed by 705
Abstract
Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution [...] Read more.
Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution of coronary heart disease rates and develops an urban heat risk index to examine underlying socioeconomic and environmental factors. We applied bivariate spatial association (Lee’s L), Global Moran’s I, and multiple linear regression methods to examine the relationships between key variables and assess model significance. Global Moran’s I revealed clustered distributions of both coronary heart disease rates and land surface temperature across census tracts. Bivariate spatial analysis identified clusters of high heart disease rates and temperatures within the West End, while clusters of contiguous suburban tracts exhibited lower heart disease rates and temperatures. Regression analyses yielded significant results for both the ordinary least squares (OLS) model and the spatial regression model; however, the spatial error model explained a greater proportion of the variation in coronary heart disease rates across tracts compared to the OLS model. This study offers new insights into spatial disparities in coronary heart disease rates and their associations with environmental risk factors including urban heat, underscoring the challenges faced by many urban communities. Full article
(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))
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16 pages, 1329 KB  
Article
Vector Data Rendering Performance Analysis of Open-Source Web Mapping Libraries
by Dániel Balla and Mátyás Gede
ISPRS Int. J. Geo-Inf. 2025, 14(9), 336; https://doi.org/10.3390/ijgi14090336 - 30 Aug 2025
Viewed by 1445
Abstract
Nowadays, various technologies exist with differing rendering performance for interactive web maps. These maps are consumed on devices with varying capabilities; therefore, choosing the best-performing library for a dataset is emphasized. Unlike existing research, this study presents a comparative analysis on libraries’ native [...] Read more.
Nowadays, various technologies exist with differing rendering performance for interactive web maps. These maps are consumed on devices with varying capabilities; therefore, choosing the best-performing library for a dataset is emphasized. Unlike existing research, this study presents a comparative analysis on libraries’ native performance for rendering large amounts of GeoJSON vector data, partially extracted from OpenStreetMap (OSM). Four libraries were analyzed. Results showed that regardless of feature types, Leaflet and OpenLayers excelled for features up to 10,000. Up to 5000 points, these two were the fastest, above which the libraries’ performance converged. For 50,000 or more, Mapbox GL JS rendered them the quickest, followed by OpenLayers, MapLibre GL JS and Leaflet. For up to 50,000 lines and 10,000 polygons, Leaflet and OpenLayers were the fastest in all scenarios. For 100,000 lines, OpenLayers was almost twice as fast as the others, while Mapbox rendered 50,000 polygons the quickest. The performance of Leaflet and OpenLayers scales with the increasing feature quantities, yet for Mapbox and MapLibre, any performance impact is offset to 1000 features and beyond. Slow initalization of map elements makes Mapbox and MapLibre less suitable for rapid rendering of small feature quantities. Other behavioural differences affecting user experience are also explored. Full article
(This article belongs to the Special Issue Cartography and Geovisual Analytics)
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21 pages, 2655 KB  
Article
A Hybrid Approach for Geo-Referencing Tweets: Transformer Language Model Regression and Gazetteer Disambiguation
by Thomas Edwards, Padraig Corcoran and Christopher B. Jones
ISPRS Int. J. Geo-Inf. 2025, 14(9), 321; https://doi.org/10.3390/ijgi14090321 - 22 Aug 2025
Viewed by 849
Abstract
Recent approaches to geo-referencing X posts have focused on the use of language modelling techniques that learn geographic region-specific language and use this to infer geographic coordinates from text. These approaches rely on large amounts of labelled data to build accurate predictive models. [...] Read more.
Recent approaches to geo-referencing X posts have focused on the use of language modelling techniques that learn geographic region-specific language and use this to infer geographic coordinates from text. These approaches rely on large amounts of labelled data to build accurate predictive models. However, obtaining significant volumes of geo-referenced data from Twitter, recently renamed X, can be difficult. Further, existing language modelling approaches can require the division of a given area into a grid or set of clusters, which can be dataset-specific and challenging for location prediction at a fine-grained level. Regression-based approaches in combination with deep learning address some of these challenges as they can assign coordinates directly without the need for clustering or grid-based methods. However, such approaches have received only limited attention for the geo-referencing task. In this paper, we adapt state-of-the-art neural network models for the regression task, focusing on geo-referencing wildlife Tweets where there is a limited amount of data. We experiment with different transfer learning techniques for improving the performance of the regression models, and we also compare our approach to recently developed Large Language Models and prompting techniques. We show that using a location names extraction method in combination with regression-based disambiguation, and purely regression when names are absent, leads to significant improvements in locational accuracy over using only regression. Full article
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21 pages, 2555 KB  
Article
Statistical Depth Measures in Density-Based Clustering with Automatic Adjustment for Skewed Data
by Mark McKenney and Daniel Tucek
ISPRS Int. J. Geo-Inf. 2025, 14(8), 298; https://doi.org/10.3390/ijgi14080298 - 30 Jul 2025
Viewed by 562
Abstract
Statistical data depth measures have been applied to density-based clustering techniques in an effort to achieve robustness in parameter selection via the affine invariant property of the depth measure. Specifically, the Mahalanobis depth measure is used in the application of DBSCAN. In this [...] Read more.
Statistical data depth measures have been applied to density-based clustering techniques in an effort to achieve robustness in parameter selection via the affine invariant property of the depth measure. Specifically, the Mahalanobis depth measure is used in the application of DBSCAN. In this paper, we examine properties of the Mahalanobis depth measure that lead to instances where it fails to detect clusters in DBSCAN, whereas Euclidean distance is able to differentiate the clusters. We propose two solutions to the problems induced by these properties. The first re-examines clusters to determine if data shape is causing multiple clusters to be grouped into a single cluster. The second examines the use of a different measure as an alternate depth function. Experiments are provided. Full article
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25 pages, 3204 KB  
Article
Assessing Spatial Digital Twins for Oil and Gas Projects: An Informed Argument Approach Using ISO/IEC 25010 Model
by Sijan Bhandari and Dev Raj Paudyal
ISPRS Int. J. Geo-Inf. 2025, 14(8), 294; https://doi.org/10.3390/ijgi14080294 - 28 Jul 2025
Viewed by 1336
Abstract
With the emergence of Survey 4.0, the oil and gas (O & G) industry is now considering spatial digital twins during their field design to enhance visualization, efficiency, and safety. O & G companies have already initiated investments in the research and development [...] Read more.
With the emergence of Survey 4.0, the oil and gas (O & G) industry is now considering spatial digital twins during their field design to enhance visualization, efficiency, and safety. O & G companies have already initiated investments in the research and development of spatial digital twins to build digital mining models. Existing studies commonly adopt surveys and case studies as their evaluation approach to validate the feasibility of spatial digital twins and related technologies. However, this approach requires high costs and resources. To address this gap, this study explores the feasibility of the informed argument method within the design science framework. A land survey data model (LSDM)-based digital twin prototype for O & G field design, along with 3D spatial datasets located in Lot 2 on RP108045 at petroleum lease 229 under the Department of Resources, Queensland Government, Australia, was selected as a case for this study. The ISO/IEC 25010 model was adopted as a methodology for this study to evaluate the prototype and Digital Twin Victoria (DTV). It encompasses eight metrics, such as functional suitability, performance efficiency, compatibility, usability, security, reliability, maintainability, and portability. The results generated from this study indicate that the prototype encompasses a standard level of all parameters in the ISO/IEC 25010 model. The key significance of the study is its methodological contribution to evaluating the spatial digital twin models through cost-effective means, particularly under circumstances with strict regulatory requirements and low information accessibility. Full article
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22 pages, 5960 KB  
Article
Application of Integrated Geospatial Analysis and Machine Learning in Identifying Factors Affecting Ride-Sharing Before/After the COVID-19 Pandemic
by Afshin Allahyari and Farideddin Peiravian
ISPRS Int. J. Geo-Inf. 2025, 14(8), 291; https://doi.org/10.3390/ijgi14080291 - 28 Jul 2025
Viewed by 870
Abstract
Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after [...] Read more.
Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article
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23 pages, 4005 KB  
Article
Exploring Unconventional 3D Geovisualization Methods for Land Suitability Assessment: A Case Study of Jihlava City
by Oldrich Bittner, Jakub Zejdlik, Jaroslav Burian and Vit Vozenilek
ISPRS Int. J. Geo-Inf. 2025, 14(7), 269; https://doi.org/10.3390/ijgi14070269 - 8 Jul 2025
Viewed by 939
Abstract
Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability [...] Read more.
Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article
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21 pages, 3075 KB  
Article
Evaluating Real-Time and Scheduled Public Transport Data: Challenges and Opportunities
by Liam Webb, Gary Higgs, Mitchel Langford and Robert Berry
ISPRS Int. J. Geo-Inf. 2025, 14(7), 243; https://doi.org/10.3390/ijgi14070243 - 25 Jun 2025
Viewed by 3236
Abstract
Scheduled timetable information has been used extensively in studies concerned with estimating travel times in accessibility research. Fewer studies to date have involved the use of real-time public transport data to help investigate the impacts of travel disruptions or cancellations of service on [...] Read more.
Scheduled timetable information has been used extensively in studies concerned with estimating travel times in accessibility research. Fewer studies to date have involved the use of real-time public transport data to help investigate the impacts of travel disruptions or cancellations of service on reported spatial and temporal patterns of accessibility. The aims of this paper are to introduce, describe, and compare the salient features and relative merits of alternative data sources relating to real-time transport data that could be utilized in such applications. By drawing attention to the potential of real-time data originating from such sources, this study makes recommendations for those considering building on the use of scheduled data to incorporate travel time reliability within transport applications. We conclude by highlighting the need for further research that explores the potential of using openly available sources of real-time traffic data in studies that incorporate accessibility analysis. Full article
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15 pages, 3095 KB  
Article
A Deep Learning Method for the Automated Mapping of Archaeological Structures from Geospatial Data: A Case Study of Delos Island
by Pavlos Fylaktos, George P. Petropoulos and Ioannis Lemesios
ISPRS Int. J. Geo-Inf. 2025, 14(6), 220; https://doi.org/10.3390/ijgi14060220 - 2 Jun 2025
Cited by 1 | Viewed by 1251
Abstract
The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, [...] Read more.
The integration of artificial intelligence (AI), specifically through convolutional neural networks (CNNs), is paving the way for significant advancements in archaeological research. This study explores the innovative application of the so-called Mask Region-based convolutional neural network (Mask R-CNN) algorithm in a GIS environment, utilizing high-resolution satellite imagery from the WorldView-3 system. By combining these state-of-the-art technologies, this study demonstrates the algorithm’s effectiveness at recognizing and segmenting the ancient structures within the archaeological site of Delos, Greece. Despite the computational constraints, the outcomes are promising, with around 25.91% of the initial vector data (434 out of 1675 polygons) successfully identified. The algorithm achieved an impressive F1 Score of 0.93% at a threshold of 0.9, indicating its high precision in differentiating specific features from their environments. This research highlights AI’s crucial role in archaeology, enabling the remote analysis of vast areas through automated or semi-automated techniques. Although these technologies cannot supplant essential on-site investigations, they can significantly enhance traditional methodologies by minimizing costs and fieldwork duration. This study also points out obstacles, such as the complexity of and variability in archaeological remains, which complicate the creation of standardized data libraries. Nevertheless, as AI technologies progress, their applications in archaeology are anticipated to broaden, fostering further innovation within the discipline. Full article
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21 pages, 6514 KB  
Article
Evacuation Behavioural Instructions with 3D Motions: Insights from Three Use Cases
by Ruihang Xie, Sisi Zlatanova, Jinwoo (Brian) Lee and André Borrmann
ISPRS Int. J. Geo-Inf. 2025, 14(5), 197; https://doi.org/10.3390/ijgi14050197 - 8 May 2025
Cited by 1 | Viewed by 2114
Abstract
During emergency evacuations, pedestrians may use three-dimensional (3D) motions, such as low crawling and climbing up/down, to navigate above or below indoor objects (e.g., tables, chairs, and stair flights). Understanding how these motions influence evacuation processes can facilitate the development of behavioural instructions. [...] Read more.
During emergency evacuations, pedestrians may use three-dimensional (3D) motions, such as low crawling and climbing up/down, to navigate above or below indoor objects (e.g., tables, chairs, and stair flights). Understanding how these motions influence evacuation processes can facilitate the development of behavioural instructions. This study examines the influence of 3D motions through a simulation-based method. This method combines a voxel-based 3D indoor model with an agent-based model. Three use case studies are elaborated upon, considering varying building types, agent numbers, urgency levels, and demographic differences. These case studies serve as exploratory demonstrations rather than validated simulations grounded in real-world evacuation experiments. Our findings are as follows: (1) Three-dimensional motions may create alternative and local 3D paths, enabling agents to bypass congestion, particularly in narrow corridors and confined spaces. (2) While 3D motions may help alleviate local congestion, they may intensify bottlenecks near exits, especially in highly crowded and high-urgency scenarios. (3) As urgency and agent numbers increase, differences in evacuation efficiency between scenarios with and without 3D motions are likely to diminish. We suggest further investigation into evacuation behavioural instructions, including the following: (1) conditional use of 3D motions in different buildings and (2) instructions tailored to different demographic groups. These use cases illustrate new directions for evacuation managers to consider the incorporation of 3D motions. Full article
(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)
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23 pages, 2596 KB  
Article
RouteLAND: An Integrated Method and a Geoprocessing Tool for Characterizing the Dynamic Visual Landscape Along Highways
by Loukas-Moysis Misthos and Vassilios Krassanakis
ISPRS Int. J. Geo-Inf. 2025, 14(5), 187; https://doi.org/10.3390/ijgi14050187 - 30 Apr 2025
Cited by 1 | Viewed by 1426
Abstract
Moving away from a static concept for the landscape that surrounds us, in this research article, we approach the visual landscape as a dynamic concept. Moreover, we attempt to provide an interconnection between the domains of landscape and cartography by designing maps that [...] Read more.
Moving away from a static concept for the landscape that surrounds us, in this research article, we approach the visual landscape as a dynamic concept. Moreover, we attempt to provide an interconnection between the domains of landscape and cartography by designing maps that are particularly suitable for characterizing the visible landscape and are potentially meaningful for overall landscape evaluation. Thus, the present work mainly focuses on the consecutive computation of vistas along highways, incorporating actual landscape composition—as the landscape is perceived from an egocentric perspective by observers moving along highway routes in peri-urban landscapes. To this end, we developed an integrated method and a Python (version 2.7.16) tool, named “RouteLAND”, for implementing an algorithmic geoprocessing procedure; through this geoprocessing tool, sequences of composite dynamic geospatial analyses and geometric calculations are automatically implemented. The final outputs are interactive web maps, whereby the segments of highway routes are characterized according to the dominant element of the visible landscape by employing (spatial) aggregation techniques. The developed geoprocessing tool and the generated interactive map provide a cartographic exploratory tool for summarizing the landscape character of highways in any peri-urban landscape, while hypothetically moving in a vehicle. In addition, RouteLAND can potentially aid in the assessment of existing or future highways’ scenic level and in the sustainable design of new highways based on the minimization of intrusive artificial structures’ vistas; in this sense, RouteLAND can serve as a valuable tool for landscape evaluation and sustainable spatial planning and development. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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29 pages, 74025 KB  
Article
Geospatial Framework for Assessing the Suitability and Demand for Agricultural Digital Solutions in Europe: A Tool for Informed Decision-Making
by Theodoros Chalazas, Antonis Koukourikos, Jan Bauwens, Nick Berkvens, Jonathan Van Beek, Nikos Kalatzis, George Papadopoulos, Panagiotis Ilias, Nikolaos Marianos and Christopher Brewster
ISPRS Int. J. Geo-Inf. 2025, 14(5), 185; https://doi.org/10.3390/ijgi14050185 - 25 Apr 2025
Viewed by 1986
Abstract
This study introduces a geospatial comprehensive methodological system aimed at evaluating the suitability and need for agricultural digital solutions (ADSs) across Europe. This system integrates a diverse range of factors, including geophysical characteristics, climate patterns, and socioeconomic conditions, evaluated at regional- and farm-specific [...] Read more.
This study introduces a geospatial comprehensive methodological system aimed at evaluating the suitability and need for agricultural digital solutions (ADSs) across Europe. This system integrates a diverse range of factors, including geophysical characteristics, climate patterns, and socioeconomic conditions, evaluated at regional- and farm-specific levels. By leveraging open-source Earth observations and socioeconomic data, we develop multiple performance, environmental, and socioeconomic similarity indexes that compare regions based on shared characteristics, such as soil quality, climate, and socioeconomic factors. Using advanced statistical and multi-criteria analysis tools, these indexes are tailored to different stages of agricultural production, enabling region-specific assessments that identify and prioritize the needs for digital solutions across Europe. The results indicate that the developed indexes effectively categorize regions based on comparable characteristics, facilitating the targeted recommendation of ADSs. Additionally, a connectivity performance index is created to assess the local deployment model of agricultural digital solutions (cloud, edge, or mixed), ensuring that the recommendations for technological implementation are feasible and effective given the local connectivity conditions. Full article
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20 pages, 5374 KB  
Article
The Urban–Rural Education Divide: A GIS-Based Assessment of the Spatial Accessibility of High Schools in Romania
by Angelo Andi Petre, Liliana Dumitrache, Alina Mareci and Alexandra Cioclu
ISPRS Int. J. Geo-Inf. 2025, 14(5), 183; https://doi.org/10.3390/ijgi14050183 - 24 Apr 2025
Cited by 4 | Viewed by 3915
Abstract
Educational achievement plays a significant role in the labour market, benefiting individuals and society. Graduating from high school is a key step towards better employment opportunities and a prerequisite for higher education attainment. In 2023, only 22.5% of the Romanian population graduated tertiary [...] Read more.
Educational achievement plays a significant role in the labour market, benefiting individuals and society. Graduating from high school is a key step towards better employment opportunities and a prerequisite for higher education attainment. In 2023, only 22.5% of the Romanian population graduated tertiary education, while 16.6% left education or training early. The Romanian public high school network comprises 1558 units, mostly located in urban areas. The high school enrolment rate is 83.5% in urban areas, and it drops to less than 60% in rural areas, with the country registering the highest out-of-school rate in the EU for the 15-year-old population. Spatial accessibility may influence enrolment in high schools, particularly for students living in rural or remote areas, who often face financial challenges fuelled by long distances and limited transportation options. Hence, travel distance may represent a potential barrier to completing the educational process or may determine inequalities in educational opportunities and outcomes. This paper aims to assess the spatial accessibility of the public high school network in Romania by using distance data provided by the Open Street Map API (Application Programming Interface). We examine variations in spatial accessibility based on the distribution of high school units and road network characteristics considering three variables: travel distance to the nearest high school, the average distance to three different categories of high schools, and the number of high schools located within a 20 km buffer zone. The results highlight a significant urban–rural divide in the availability of public high school facilities, with 84.1% (n = 1311) located in urban areas while 49.1% of the high school-aged population lives in rural areas. Many rural communities lack adequate educational facilities, often having limited options for high school education. The findings also show that 32% of the high school-aged population has to travel more than 10 km to the nearest high school, and 7% has no high school options within a 20 km buffer zone. This study provides insights into the educational landscape in Romania, pointing out areas with limited access to high schools, which contributes to further inequalities in educational attainment. The findings may serve as a basis for developing policies and practices to bridge the urban–rural divide in educational opportunities and foster a more equitable and inclusive education system. Full article
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39 pages, 7188 KB  
Review
Georeferencing Building Information Models for BIM/GIS Integration: A Review of Methods and Tools
by Peyman Azari, Songnian Li, Ahmed Shaker and Shahram Sattar
ISPRS Int. J. Geo-Inf. 2025, 14(5), 180; https://doi.org/10.3390/ijgi14050180 - 22 Apr 2025
Viewed by 4143
Abstract
With the rise of urban digital twins and smart cities, the integration of building information modeling (BIM) and geospatial information systems (GISs) have captured the interest of researchers. Although significant advancements have been achieved in this field, challenges persist in the georeferencing of [...] Read more.
With the rise of urban digital twins and smart cities, the integration of building information modeling (BIM) and geospatial information systems (GISs) have captured the interest of researchers. Although significant advancements have been achieved in this field, challenges persist in the georeferencing of BIM models, which is one of the fundamental challenges in integrating BIM and GIS models. These challenges stem from dissimilarities between the BIM and GIS domains, including different georeferencing definitions, different coordinate systems utilization, and a lack of correspondence between the engineering system of BIM and the project’s geographical location. This review critically examines the significance of georeferencing within this integration, outlines and compares various methods for georeferencing BIM data in detail, and surveys existing software tools that facilitate this process. The findings underscore the need for increased attention to georeferencing issues from both domains, aiming to enhance the seamless integration of BIM and GIS. Full article
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26 pages, 6562 KB  
Article
A Model of Building Changes to Support Comparative Studies and Open Discussions on Densification
by Bénédicte Bucher, Juste Raimbault, Mouhamadou Ndim, Ana-Maria Raimond, Julien Perret, Sebastian Dembski and Mathias Jehling
ISPRS Int. J. Geo-Inf. 2025, 14(4), 155; https://doi.org/10.3390/ijgi14040155 - 2 Apr 2025
Viewed by 1131
Abstract
Densification is a widely used concept, but there is a lack of terminology and tools to facilitate discussions among data scientists, policy makers and citizens. This paper proposes a model of building changes based on building surveys undertaken in past decades to connect [...] Read more.
Densification is a widely used concept, but there is a lack of terminology and tools to facilitate discussions among data scientists, policy makers and citizens. This paper proposes a model of building changes based on building surveys undertaken in past decades to connect discussions about densification with shared evidence. A specific challenge is to process buildings in city regions and areas in a replicable way across different building data sources. Another challenge is to manage the quality of the representation, i.e., how well the maps represent changes to buildings and how well they can support discussions of densification. Building data and real buildings are different things that sometimes change in an independent way. Addressing these factors requires different forms of expertise, i.e., expertise about the realities depicted in the areas studied, about local data sources, and about advanced matching tools and state-of-the-art densification concepts. We present a collaborative dashboard through which to engage corresponding experts in the production of building change maps and the clarification of related concepts. Full article
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19 pages, 2850 KB  
Article
Use and Effectiveness of Chatbots as Support Tools in GIS Programming Course Assignments
by Hartwig H. Hochmair
ISPRS Int. J. Geo-Inf. 2025, 14(4), 156; https://doi.org/10.3390/ijgi14040156 - 2 Apr 2025
Cited by 2 | Viewed by 4719
Abstract
Advancements in large language models have significantly transformed higher education by integrating AI chatbots into course design, teaching, administration, and student support. This study evaluates the use, effectiveness, and perceptions of chatbots in a Python-based graduate-level GIS programming course at a U.S. university. [...] Read more.
Advancements in large language models have significantly transformed higher education by integrating AI chatbots into course design, teaching, administration, and student support. This study evaluates the use, effectiveness, and perceptions of chatbots in a Python-based graduate-level GIS programming course at a U.S. university. Students self-reported perceived improvements in skills and the use of different help resources across three home assignments of varying complexity and spatial context. In group discussions, students shared their experiences, strategies, and envisioned future applications of chatbots in GIS programming and beyond. The results indicate that prior programming experience enhances students’ perception of assignment usefulness, and that chatbots serve as a partial replacement for traditional help resources (e.g., websites) in completing assignments. Overall, students expressed positive sentiments regarding chatbot effectiveness, especially for complex spatial tasks. While students were optimistic about the potential of chatbots to enhance future learning, concerns were raised about overreliance on AI, which could hinder the development of independent problem-solving and programming skills. In conclusion, this study offers valuable insights into optimizing chatbot integration in GIS education. Full article
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27 pages, 2788 KB  
Article
Critical Success Factors of Participatory Community Planning with Geospatial Digital Participatory Platforms
by Karl Atzmanstorfer, Mona Bartling, Barbora Haltofová, Leo Zurita-Arthos, Judith Grubinger-Preiner and Anton Eitzinger
ISPRS Int. J. Geo-Inf. 2025, 14(4), 153; https://doi.org/10.3390/ijgi14040153 - 1 Apr 2025
Cited by 1 | Viewed by 1803
Abstract
In recent years, Digital Participatory Platforms (DPPs) have become an increasingly popular tool for citizen participation in community planning processes. They serve municipalities, citizen initiatives, and other planning authorities as digital tools to collect feedback, discuss ideas, solve problems and monitor small-scale planning [...] Read more.
In recent years, Digital Participatory Platforms (DPPs) have become an increasingly popular tool for citizen participation in community planning processes. They serve municipalities, citizen initiatives, and other planning authorities as digital tools to collect feedback, discuss ideas, solve problems and monitor small-scale planning processes within their communities. In addition, DPPs facilitate the integration of the spatial domain into participatory community planning. In this paper, we assess the most important Critical Success Factors (CSFs) of participatory community planning with geospatial DPPs, and analyze the potential, opportunities, and challenges associated with integrating these platforms into community planning. We analyze the results of a digital questionnaire that we shared with a selected group of expert scholars and community stakeholders. We then contextualize this feedback with our experiences from the piloting phase and commercial roll-out of the ‘Bürgercockpit’-application for participatory community planning within the Austrian Agenda21-framework. As a result, we identify the most important CSFs of participatory community planning with geospatial DPPs. This set of CSFs should provide a better orientation on how to complement well-established analog participatory methods and practices with geospatial DPPs for the co-production of shared visions and solutions, ultimately empowering all stakeholders of a planning process to better manage their communities. Full article
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19 pages, 10779 KB  
Article
Conceptual Neighborhood Graphs of Topological Relations in Z2
by Brendan Patrick Hall and Matthew Paul Dube
ISPRS Int. J. Geo-Inf. 2025, 14(4), 150; https://doi.org/10.3390/ijgi14040150 - 31 Mar 2025
Cited by 1 | Viewed by 851
Abstract
Topological relations form the backbone of qualitative spatial reasoning and, as such, play a paramount role in geographic information systems. Three decades of research have provided a proliferation of sets of qualitative topological relations in both continuous and discretized spaces, but only in [...] Read more.
Topological relations form the backbone of qualitative spatial reasoning and, as such, play a paramount role in geographic information systems. Three decades of research have provided a proliferation of sets of qualitative topological relations in both continuous and discretized spaces, but only in continuous spaces has the concept of organizing these relations into a larger framework (called a conceptual neighborhood graph) been considered. Previous work leveraged matrix differences to derive the anisotropic scaling neighborhood for these relations. In this paper, a simulation protocol is used to derive conceptual neighborhood graphs of qualitative topological relations in Z2 for the operations of translation and isotropic scaling. It is further shown that, when aggregating raster relations into their continuous counterparts and collapsing neighborhood connections within these groups, the familiar conceptual neighborhood structures for continuous regions appear. Full article
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30 pages, 16455 KB  
Article
Automated Detection of Pedestrian and Bicycle Lanes from High-Resolution Aerial Images by Integrating Image Processing and Artificial Intelligence (AI) Techniques
by Richard Boadu Antwi, Prince Lartey Lawson, Michael Kimollo, Eren Erman Ozguven, Ren Moses, Maxim A. Dulebenets and Thobias Sando
ISPRS Int. J. Geo-Inf. 2025, 14(4), 135; https://doi.org/10.3390/ijgi14040135 - 23 Mar 2025
Viewed by 1722
Abstract
The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent [...] Read more.
The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent improvements in computational power and image recognition techniques, there are now reliable methods to identify and map various roadway elements from multiple imagery sources. Notably, comprehensive geospatial data for pedestrian and bicycle lanes are still lacking across many state and local roadways, including those in the State of Florida, despite the essential role this information plays in optimizing traffic efficiency and reducing crashes. Developing fast, efficient methods to gather this data are essential for transportation agencies as they also support objectives like identifying outdated or obscured markings, analyzing pedestrian and bicycle lane placements relative to crosswalks, turning lanes, and school zones, and assessing crash patterns in the associated areas. This study introduces an innovative approach using deep neural network models in image processing and computer vision to detect and extract pedestrian and bicycle lane features from very high-resolution aerial imagery, with a focus on public roadways in Florida. Using YOLOv5 and MTRE-based deep learning models, this study extracts and segments bicycle and pedestrian features from high-resolution aerial images, creating a geospatial inventory of these roadway features. Detected features were post-processed and compared with ground truth data to evaluate performance. When tested against ground truth data from Leon County, Florida, the models demonstrated accuracy rates of 73% for pedestrian lanes and 89% for bicycle lanes. This initiative is vital for transportation agencies, enhancing infrastructure management by enabling timely identification of aging or obscured lane markings, which are crucial for maintaining safe transportation networks. Full article
(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)
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37 pages, 7441 KB  
Review
Hexahedral Projections: A Comprehensive Review and Ranking
by Aleksandar Dimitrijević and Peter Strobl
ISPRS Int. J. Geo-Inf. 2025, 14(3), 122; https://doi.org/10.3390/ijgi14030122 - 6 Mar 2025
Viewed by 1795
Abstract
Hexahedral projections—mapping the Earth’s surface onto the faces of a circumscribed cube—have drawn scientific interest for over half a century. During this time, numerous projections with diverse characteristics have been developed. This paper provides the most comprehensive review of these projections to date, [...] Read more.
Hexahedral projections—mapping the Earth’s surface onto the faces of a circumscribed cube—have drawn scientific interest for over half a century. During this time, numerous projections with diverse characteristics have been developed. This paper provides the most comprehensive review of these projections to date, offering a detailed examination of the processes involved in projecting the Earth onto a cube, with a focus on distortion and accuracy. A numerical and graphical analysis of the characteristics of hexahedral projections is presented, serving as the foundation for a composite hierarchical metric based on ranking. This metric is used to rank hexahedral projections according to individual criteria, groups of criteria, and overall performance. Full article
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20 pages, 17194 KB  
Article
Understanding the Carbon Footprint of Tile Transfer for Web Maps
by Guillaume Touya, Azelle Courtial, Jérémy Kalsron, Justin Berli, Bérénice Le Mao and Laura Wenclik
ISPRS Int. J. Geo-Inf. 2025, 14(3), 107; https://doi.org/10.3390/ijgi14030107 - 1 Mar 2025
Cited by 3 | Viewed by 1202
Abstract
As web maps are now extensively used by billions of users, the energy consumption of these maps is not marginal anymore. Green cartography seeks to reduce the energy consumption of maps to promote more sustainable digital tools. To reduce energy consumption, we first [...] Read more.
As web maps are now extensively used by billions of users, the energy consumption of these maps is not marginal anymore. Green cartography seeks to reduce the energy consumption of maps to promote more sustainable digital tools. To reduce energy consumption, we first need to better understand the different sources of energy consumption for web maps. Among these sources, this paper focuses on the tiles that are stored on servers and then constantly transferred each time a user explores the map. This paper presents several experiments carried out with current web maps to assess this energy consumption. We first try to assess the number of map tiles that are loaded through the web when users explore web maps, and we determine which types of interaction are used with the maps, and a similar amount of tiles is loaded. Then, we try to assess which zoom levels are the most loaded by users; it appears that the medium–large scales are the most used (between zoom levels 11 and 17). Then, we explore the size of the map tiles and try to assess which ones are larger and thus require more energy to load over the web; we can find clear differences between zoom levels. Finally, we discuss how map generalization could be used to reduce energy consumption by creating lighter tiles. These experiments show that the current web maps are suboptimal regarding energy consumption, with many tiles loaded at zoom levels where the tiles are larger than necessary. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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23 pages, 9017 KB  
Article
Climate Change Maps for the Atlas of Switzerland
by Luca Gaia, Andreas Neumann and Lorenz Hurni
ISPRS Int. J. Geo-Inf. 2025, 14(3), 99; https://doi.org/10.3390/ijgi14030099 - 22 Feb 2025
Cited by 1 | Viewed by 2508
Abstract
Climate change has global consequences, and Switzerland is no exception. The communication of climate change poses various challenges, and maps are often part of this process. This work presents three maps illustrating the impacts of climate change, developed for the Atlas of Switzerland [...] Read more.
Climate change has global consequences, and Switzerland is no exception. The communication of climate change poses various challenges, and maps are often part of this process. This work presents three maps illustrating the impacts of climate change, developed for the Atlas of Switzerland (AoS), an interactive digital national atlas. The aim is to make climate change impacts understandable and visible. Three different indicators of climate change were visualized: the rise of the zero degree line, the evolution of glacial lakes, and changes in the flowering dates of plants. Various approaches were employed that leverage the strengths of the AoS, including temporal navigation, interactivity, 3D data visualizations, and map combinations. The feasibility of these visualizations are demonstrated through the presented maps and analysis of key considerations for their creation. We believe these map types should be included in national atlases and can contribute to the achievement of Sustainable Development Goal 13: “Climate Action”. Further research is needed to assess the effectiveness and user understandability of the proposed maps. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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16 pages, 1482 KB  
Article
Mobile Cadastral Application with Open-Source Software in Colombia
by Gaspar Mora-Navarro, Carmen Femenia-Ribera, Enric Terol and Cristhian Quiza-Neuto
ISPRS Int. J. Geo-Inf. 2025, 14(3), 96; https://doi.org/10.3390/ijgi14030096 - 20 Feb 2025
Cited by 1 | Viewed by 1900
Abstract
This article presents social research, conducted through interviews with experts involved in land administration in Colombia, on the possibility of using the Fit-For-Purpose methodology, combined with indirect methods, to accelerate the capture of cadastral data. The experts were asked about the design of [...] Read more.
This article presents social research, conducted through interviews with experts involved in land administration in Colombia, on the possibility of using the Fit-For-Purpose methodology, combined with indirect methods, to accelerate the capture of cadastral data. The experts were asked about the design of a data capture system, using a mobile application, to acquire data on properties and their approximate coordinates, as well as the data of their owners, where the owners themselves are the ones who declare these data. A functional prototype has also been developed and tested in Spain. Results: The design is well received, understood as a declaration by owners, especially in rural areas; further processing of the information by technicians of the competent authority is necessary; involving the population has a positive impact on the perception that owners have regarding cadastral processes; some technical and training challenges must be taken into account, to ensure consistency and quality in the data collected; and the prototype tests demonstrate, due to the low GPS accuracy of mobile phones, that the identification of boundaries over a base map is possible in properties of one hectare or more. Full article
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19 pages, 22324 KB  
Article
Beyond the Road: A Regional Perspective on Traffic Congestion in Metro Atlanta
by Jeong Chang Seong, Seungyeon Lee, Yoonjae Cho and Chulsue Hwang
ISPRS Int. J. Geo-Inf. 2025, 14(2), 61; https://doi.org/10.3390/ijgi14020061 - 3 Feb 2025
Viewed by 4956
Abstract
Traffic congestion not only affects traffic flow but also influences public perception of congested regions. While analyzing congestion at the road section level can help identify engineering solutions, it often fails to reveal broader spatial patterns and trends at the regional or macro [...] Read more.
Traffic congestion not only affects traffic flow but also influences public perception of congested regions. While analyzing congestion at the road section level can help identify engineering solutions, it often fails to reveal broader spatial patterns and trends at the regional or macro scale unless summarized effectively. This study aims to address these challenges by focusing on regional-scale traffic congestion amounts measured by distanceTime metrics. A 12–month dataset, sampled every 10 min, was analyzed to identify spatial patterns, temporal trends, regional variations, and predictive models in the Metro Atlanta area. The results show that congestion is the most severe and increasing at key urban corridors like Brookhaven–Sandy Springs, the downtown connector, Druid Hills–Decatur, and Johns Creek–Cumming, aligning with recent urban developments. Cities such as Alpharetta, Dunwoody, Brookhaven, Austell, Stone Mountain, East Point, Lake City, Morrow, Fairburn, and Jonesboro show high increasing trends in congestion. Predictive modeling with the long short-term memory (LSTM) method shows promising results for short-term forecasts, though variability in data requires further optimization for certain cities. This research is significant because it demonstrates that congestion amounts measured by distanceTime metrics can be used for assessing regional characteristics broadly at a metropolitan city scale. The findings and methodologies identified in this research might support urban and transportation planning efforts in metropolitan planning organizations, such as the Atlanta Regional Commission, by identifying congestion amounts and trends at both the regional and road scales. Full article
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23 pages, 19140 KB  
Article
Enhancing Spatial Awareness and Collaboration: A Guide to VR-Ready Survey Data Transformation
by Joseph Kevin McDuff, Armin Agha Karimi and Zahra Gharineiat
ISPRS Int. J. Geo-Inf. 2025, 14(2), 59; https://doi.org/10.3390/ijgi14020059 - 2 Feb 2025
Cited by 1 | Viewed by 2152
Abstract
Surveying and spatial science are experiencing a paradigm shift from traditional data outputs to more immersive and interactive formats, driven by the rise in Virtual Reality (VR). This study addresses the challenge of transforming UAV (Unmanned Aerial Vehicle)-acquired photogrammetry data into VR-compatible surfaces [...] Read more.
Surveying and spatial science are experiencing a paradigm shift from traditional data outputs to more immersive and interactive formats, driven by the rise in Virtual Reality (VR). This study addresses the challenge of transforming UAV (Unmanned Aerial Vehicle)-acquired photogrammetry data into VR-compatible surfaces while preserving the accuracy and quality crucial to professional surveying. The study leverages Blender, an open-source 3D creation tool, to develop a procedural guide for creating VR-ready models from high-quality survey data. The case study focuses on silos located in Yelarbon, Southeast Queensland, Australia. UAV mapping is utilised to gather the data necessary for 3D modelling with a few minor alterations in the photo capturing angle and processing. Key findings reveal that while Blender excels as a visualisation tool, it struggles with geospatial precision, particularly when handling large numbers coming from coordinate systems, leading to rounding errors seen within the VR model. Blender’s strength lies in creating immersive experiences for public engagement but is constrained by its lack of capability to hold survey metadata, hindering its applicability for professional survey-grade outputs. The results highlight the need for further development into possible Blender plugins that integrate geospatial accuracy with VR outputs. This study underscores the potential of VR to enhance how survey data are visualised, offering opportunities for future innovations in both the technical and creative aspects of the surveying profession. Full article
(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)
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25 pages, 17627 KB  
Article
The Machine Learning-Based Mapping of Urban Pluvial Flood Susceptibility in Seoul Integrating Flood Conditioning Factors and Drainage-Related Data
by Julieber T. Bersabe and Byong-Woon Jun
ISPRS Int. J. Geo-Inf. 2025, 14(2), 57; https://doi.org/10.3390/ijgi14020057 - 1 Feb 2025
Cited by 3 | Viewed by 5699
Abstract
In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors [...] Read more.
In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors is vital for identifying flood-prone areas and developing predictive models in highly urbanized regions. This study evaluates and maps urban pluvial flood susceptibility in Seoul, South Korea using the machine learning techniques such as logistic regression (LR), random forest (RF), and support vector machines (SVM), and integrating traditional flood conditioning factors and drainage-related data. Together with known flooding points from 2010 to 2022, sixteen flood conditioning factors were selected, including the drainage-related parameters sewer pipe density (SPD) and distance to a storm drain (DSD). The RF model performed best (accuracy: 0.837, an area under the receiver operating characteristic curve (AUC): 0.902), and indicated that 32.65% of the study area has a high susceptibility to flooding. The accuracy and AUC were improved by 7.58% and 3.80%, respectively, after including the two drainage-related variables in the model. This research provides valuable insights for urban flood management, highlighting the primary causes of flooding in Seoul and identifying areas with heightened flood susceptibility, particularly relating to drainage infrastructure. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)
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33 pages, 10796 KB  
Article
Use of Semantic Web Technologies to Enhance the Integration and Interoperability of Environmental Geospatial Data: A Framework Based on Ontology-Based Data Access
by Sajith Ranatunga, Rune Strand Ødegård, Knut Jetlund and Erling Onstein
ISPRS Int. J. Geo-Inf. 2025, 14(2), 52; https://doi.org/10.3390/ijgi14020052 - 28 Jan 2025
Cited by 2 | Viewed by 3339
Abstract
This study addresses the challenges of integrating heterogeneous environmental geospatial data by proposing a framework based on ontology-based data access (OBDA). Geospatial data are important for decision-making in various domains, such as environmental monitoring, disaster management, and urban development. Data integration is a [...] Read more.
This study addresses the challenges of integrating heterogeneous environmental geospatial data by proposing a framework based on ontology-based data access (OBDA). Geospatial data are important for decision-making in various domains, such as environmental monitoring, disaster management, and urban development. Data integration is a common challenge within these domains due to data heterogeneity and semantic discrepancies. The proposed framework uses semantic web technologies to enhance data interoperability, accessibility, and usability. Several practical examples were demonstrated to validate its effectiveness. These examples were based in Lake Mjøsa, Norway, addressing both spatial and non-spatial scenarios to test the framework’s potential. By extending the GeoSPARQL ontology, the framework supports SPARQL queries to retrieve information based on user requirements. A web-based SPARQL Query Interface (SQI) was developed to execute queries and display the retrieved data in tabular and visual format. Utilizing free and open-source software (FOSS), the framework is easily replicable for stakeholders and researchers. Despite some limitations, the study concludes that the framework is able to enhance cross-domain data integration and semantic querying in various informed decision-making scenarios. Full article
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19 pages, 7846 KB  
Article
A GIS-Based Framework to Analyze the Behavior of Urban Greenery During Heatwaves Using Satellite Data
by Barbara Cardone, Ferdinando Di Martino, Cristiano Mauriello and Vittorio Miraglia
ISPRS Int. J. Geo-Inf. 2024, 13(11), 377; https://doi.org/10.3390/ijgi13110377 - 30 Oct 2024
Cited by 1 | Viewed by 2187
Abstract
This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land [...] Read more.
This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements. Full article
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29 pages, 38136 KB  
Article
Constructing Efficient Mesh-Based Global Grid Systems with Reduced Distortions
by Lakin Wecker, John Hall and Faramarz F. Samavati
ISPRS Int. J. Geo-Inf. 2024, 13(11), 373; https://doi.org/10.3390/ijgi13110373 - 22 Oct 2024
Cited by 1 | Viewed by 2359
Abstract
Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial [...] Read more.
Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial capabilities. Current DGGSs employ a simple, low-resolution polyhedral approximation of the Earth for efficient operations, but require a projection between the Earth’s surface and the polyhedral faces. Equal-area DGGSs are desirable for their low distortion, but they fall short of this promise due to the inefficiency of equal-area projections. On the other hand, efficiency-first DGGSs need to better address distortion. We introduce a novel mesh-based DGGS (MBD) which generalizes efficient operations over watertight triangular meshes with spherical topology. Unlike traditional approaches that rely on Platonic or Catalan solids, our mesh-based method leverages high-resolution spherical meshes to offer greater flexibility and accuracy. MBD allows high-resolution polyhedra (HRP) to be used as the base polyhedron of a DGGS, significantly reducing distortion. To address the operational challenges, we introduce a new hash encoding method and an efficient barycentric indexing method (BIM). MBD extends Atlas of Connectivity Maps to the BIM to provide efficient spatial and hierarchical traversal. We introduce several new base polyhedra with lower areal and angular distortion, and we experimentally validate their properties and demonstrate their efficiency. Our experimentation shows that we achieve constant-time operations for high-resolution MBD, and we recommend polyhedra to be used as the base polyhedron for low-distortion DGGSs, compact faces, and efficient operations. Full article
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36 pages, 13506 KB  
Article
ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models
by Ali Mansourian and Rachid Oucheikh
ISPRS Int. J. Geo-Inf. 2024, 13(10), 348; https://doi.org/10.3390/ijgi13100348 - 1 Oct 2024
Cited by 19 | Viewed by 14865
Abstract
Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to [...] Read more.
Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis. Full article
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24 pages, 210054 KB  
Article
Scale- and Resolution-Adapted Shaded Relief Generation Using U-Net
by Marianna Farmakis-Serebryakova, Magnus Heitzler and Lorenz Hurni
ISPRS Int. J. Geo-Inf. 2024, 13(9), 326; https://doi.org/10.3390/ijgi13090326 - 12 Sep 2024
Cited by 1 | Viewed by 2160
Abstract
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) [...] Read more.
On many maps, relief shading is one of the most significant graphical elements. Modern relief shading techniques include neural networks. To generate such shading automatically at an arbitrary scale, one needs to consider how the resolution of the input digital elevation model (DEM) relates to the neural network process and the maps used for training. Currently, there is no clear guidance on which DEM resolution to use to generate relief shading at specific scales. To address this gap, we trained the U-Net models on swisstopo manual relief shadings of Switzerland at four different scales and using four different resolutions of SwissALTI3D DEM. An interactive web application designed for this study allows users to outline a random area and compare histograms of varying brightness between predictions and manual relief shadings. The results showed that DEM resolution and output scale influence the appearance of the relief shading, with an overall scale/resolution ratio. We present guidelines for generating relief shading with neural networks for arbitrary areas and scales. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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28 pages, 37910 KB  
Article
Cultural Heritage in Times of Crisis: Damage Assessment in Urban Areas of Ukraine Using Sentinel-1 SAR Data
by Ute Bachmann-Gigl and Zahra Dabiri
ISPRS Int. J. Geo-Inf. 2024, 13(9), 319; https://doi.org/10.3390/ijgi13090319 - 5 Sep 2024
Cited by 3 | Viewed by 3399
Abstract
Cultural property includes immovable assets that are part of a nation’s cultural heritage and reflect the cultural identity of a people. Hence, information about armed conflict’s impact on historical buildings’ structures and heritage sites is extremely important. The study aims to demonstrate the [...] Read more.
Cultural property includes immovable assets that are part of a nation’s cultural heritage and reflect the cultural identity of a people. Hence, information about armed conflict’s impact on historical buildings’ structures and heritage sites is extremely important. The study aims to demonstrate the application of Earth observation (EO) synthetic aperture radar (SAR) technology, and in particular Sentinel-1 SAR coherence time-series analysis, to monitor spatial and temporal changes related to the recent Russian–Ukrainian war in the urban areas of Mariupol and Kharkiv, Ukraine. The study considers key events during the siege of Mariupol and the battle of Kharkiv from February to May 2022. Built-up areas and cultural property were identified using freely available OpenStreetMap (OSM) data. Semi-automated coherent change-detection technique (CCD) that utilize difference analysis of pre- and co-conflict coherences were capable of highlighting areas of major impact on the urban structures. The study applied a logistic regression model (LRM) for the discrimination of damaged and undamaged buildings based on an estimated likelihood of damage occurrence. A good agreement was observed with the reference data provided by the United Nations Satellite Centre (UNOSAT) in terms of the overall extent of damage. Damage maps enable the localization of buildings and cultural assets in areas with a high probability of damage and can serve as the basis for a high-resolution follow-up investigation. The study reveals the benefits of Sentinel-1 SAR CCD in the sense of unsupervised delineation of areas affected by armed conflict. However, limitations arise in the detection of local and single-building damage compared to regions with large-scale destruction. The proposed semi-automated multi-temporal Sentinel-1 data analysis using CCD methodology shows its applicability for the timely investigation of damage to buildings and cultural heritage, which can support the response to crises. Full article
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17 pages, 7654 KB  
Article
The Impact of Airbnb on Long-Term Rental Markets in San Francisco: A Geospatial Analysis Using Multiscale Geographically Weighted Regression
by Dongkeun Hur, Seonjin Lee and Hany Kim
ISPRS Int. J. Geo-Inf. 2024, 13(9), 298; https://doi.org/10.3390/ijgi13090298 - 23 Aug 2024
Viewed by 4313
Abstract
The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap [...] Read more.
The rapid proliferation of peer-to-peer short-term vacation rentals has sparked a debate regarding their impact on housing markets. This study further investigates this issue by examining the effect of Airbnb on relative rent costs in San Francisco. The research addresses a critical gap in understanding whether Airbnb financially burdens local renters within different income groups. The authors also differentiated the effect of Airbnb accommodations with different levels of commercialization by categorizing Airbnb listings based on their level of commercialization. Using the multiscale geographically weighted regression technique, this study also considered spatial variations in the relationship between short- and long-term rental markets. The findings indicate that the density of Airbnb only affects the relative rent of renters with a yearly household income between USD 50,000 and USD 75,000. Furthermore, the density of Airbnb listings from more commercialized hosts that own between three and eleven showed a positive relationship with the relative rent cost. This study highlighted the variability in the impact of Airbnb on the local community by income group, listing characteristic, and geographic region. This finding underscores the need for differentiated regulation toward peer-to-peer accommodations, as the impact on rent affordability varies by host commercialization level and renter income group. Full article
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27 pages, 20774 KB  
Article
Genetic Programming to Optimize 3D Trajectories
by André Kotze, Moritz Jan Hildemann, Vítor Santos and Carlos Granell
ISPRS Int. J. Geo-Inf. 2024, 13(8), 295; https://doi.org/10.3390/ijgi13080295 - 20 Aug 2024
Viewed by 2632
Abstract
Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal [...] Read more.
Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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26 pages, 9857 KB  
Article
Spatiotemporal Analysis of Nighttime Crimes in Vienna, Austria
by Jiyoung Lee, Michael Leitner and Gernot Paulus
ISPRS Int. J. Geo-Inf. 2024, 13(7), 247; https://doi.org/10.3390/ijgi13070247 - 10 Jul 2024
Cited by 3 | Viewed by 6613
Abstract
Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during [...] Read more.
Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis. Full article
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20 pages, 8876 KB  
Article
A Comprehensive Survey on High-Definition Map Generation and Maintenance
by Kaleab Taye Asrat and Hyung-Ju Cho
ISPRS Int. J. Geo-Inf. 2024, 13(7), 232; https://doi.org/10.3390/ijgi13070232 - 1 Jul 2024
Cited by 6 | Viewed by 7009
Abstract
The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully [...] Read more.
The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully automated vehicles in the foreseeable future. High-definition (HD) maps are central to this endeavor, offering centimeter-level accuracy in mapping the environment and enabling precise localization. Unlike conventional maps, these highly detailed HD maps are critical for autonomous vehicle decision-making, ensuring safe and accurate navigation. Compiled before testing and regularly updated, HD maps meticulously capture environmental data through various methods. This study explores the vital role of HD maps in autonomous driving, delving into their creation, updating processes, and the challenges and future directions in this rapidly evolving field. Full article
(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)
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19 pages, 6750 KB  
Article
A Sensor-Based Simulation Method for Spatiotemporal Event Detection
by Yuqin Jiang, Andrey A. Popov, Zhenlong Li, Michael E. Hodgson and Binghu Huang
ISPRS Int. J. Geo-Inf. 2024, 13(5), 141; https://doi.org/10.3390/ijgi13050141 - 23 Apr 2024
Cited by 2 | Viewed by 2321
Abstract
Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the [...] Read more.
Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as “sensors”, which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur. Full article
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21 pages, 11156 KB  
Article
Map Reading and Analysis with GPT-4V(ision)
by Jinwen Xu and Ran Tao
ISPRS Int. J. Geo-Inf. 2024, 13(4), 127; https://doi.org/10.3390/ijgi13040127 - 11 Apr 2024
Cited by 16 | Viewed by 8516
Abstract
In late 2023, the image-reading capability added to a Generative Pre-trained Transformer (GPT) framework provided the opportunity to potentially revolutionize the way we view and understand geographic maps, the core component of cartography, geography, and spatial data science. In this study, we explore [...] Read more.
In late 2023, the image-reading capability added to a Generative Pre-trained Transformer (GPT) framework provided the opportunity to potentially revolutionize the way we view and understand geographic maps, the core component of cartography, geography, and spatial data science. In this study, we explore reading and analyzing maps with the latest version of GPT-4-vision-preview (GPT-4V), to fully evaluate its advantages and disadvantages in comparison with human eye-based visual inspections. We found that GPT-4V is able to properly retrieve information from various types of maps in different scales and spatiotemporal resolutions. GPT-4V can also perform basic map analysis, such as identifying visual changes before and after a natural disaster. It has the potential to replace human efforts by examining batches of maps, accurately extracting information from maps, and linking observed patterns with its pre-trained large dataset. However, it is encumbered by limitations such as diminished accuracy in visual content extraction and a lack of validation. This paper sets an example of effectively using GPT-4V for map reading and analytical tasks, which is a promising application for large multimodal models, large language models, and artificial intelligence. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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36 pages, 2828 KB  
Review
Framing VRRSability Relationships among Vulnerability, Risk, Resilience, and Sustainability for Improving Geo-Information Evaluations within Geodesign Decision Support
by Timothy Nyerges, John A. Gallo, Keith M. Reynolds, Steven D. Prager, Philip J. Murphy and Wenwen Li
ISPRS Int. J. Geo-Inf. 2024, 13(3), 67; https://doi.org/10.3390/ijgi13030067 - 23 Feb 2024
Viewed by 3040
Abstract
Improving geo-information decision evaluation is an important part of geospatial decision support research, particularly when considering vulnerability, risk, resilience, and sustainability (V-R-R-S) of urban land–water systems (ULWSs). Previous research enumerated a collection of V-R-R-S conceptual component commonalties and differences resulting in a synthesis [...] Read more.
Improving geo-information decision evaluation is an important part of geospatial decision support research, particularly when considering vulnerability, risk, resilience, and sustainability (V-R-R-S) of urban land–water systems (ULWSs). Previous research enumerated a collection of V-R-R-S conceptual component commonalties and differences resulting in a synthesis concept called VRRSability. As a single concept, VRRSability enhances our understanding of the relationships within and among V-R-R-S. This paper reports research that extends and deepens the VRRSability synthesis by elucidating relationships among the V-R-R-S concepts, and organizes them into a VRRSability conceptual framework meant to guide operationalization within decision support systems. The core relationship within the VRRSability framework is ‘functional performance’, which couples land and water concerns within complex ULWS. Using functional performance, we elucidate other significant conceptual relationships, e.g., scale, scenarios and social knowledge, among others. A narrative about the functional performance of green stormwater infrastructure as part of a ULWS offers a practical application of the conceptual framework. VRRSability decision evaluation trade-offs among land and water emerge through the narrative, particularly how land cover influences water flow, which in turn influences water quality. The discussion includes trade-offs along risk–resilience and vulnerability–sustainability dimensions as key aspects of functional performance. Conclusions include knowledge contributions about a VRRSability conceptual framework and the next steps for operationalization within decision support systems using artificial intelligence. Full article
(This article belongs to the Special Issue Innovative GIS Models and Approaches for Large Environmental and Urban Applications in the Age of AI)
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41 pages, 7235 KB  
Article
Temporal Paths in Real-World Sensor Networks
by Erik Bollen, Bart Kuijpers, Valeria Soliani and Alejandro Vaisman
ISPRS Int. J. Geo-Inf. 2024, 13(2), 36; https://doi.org/10.3390/ijgi13020036 - 24 Jan 2024
Viewed by 2300
Abstract
Sensor networks are used in an increasing number and variety of application areas, like traffic control or river monitoring. Sensors in these networks measure parameters of interest defined by domain experts and send these measurements to a central location for storage, viewing and [...] Read more.
Sensor networks are used in an increasing number and variety of application areas, like traffic control or river monitoring. Sensors in these networks measure parameters of interest defined by domain experts and send these measurements to a central location for storage, viewing and analysis. Temporal graph data models, whose nodes contain time-series data reported by the sensors, have been proposed to model and analyze these networks in order to take informed and timely decisions on their operation. Temporal paths are first-class citizens in this model and some classes of them have been identified in the literature. Queries aimed at finding these paths are denoted as (temporal) path queries. In spite of these efforts, many interesting problems remain open and, in this work, we aim at answering some of them. More concretely, we characterize the classes of temporal paths that can be defined in a sensor network in terms of the well-known Allen’s temporal algebra. We also show that, out of the 8192 possible interval relations in this algebra, only 11 satisfy two desirable properties that we define: transitivity and robustness. We show how these properties and the paths that satisfy them are relevant in practice by means of a real-world use case consisting of an analysis of salinity that appears close to the Scheldt river in Flanders, Belgium, during high tides occurring in the North Sea. Full article
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33 pages, 17787 KB  
Article
Improving Three-Dimensional Building Segmentation on Three-Dimensional City Models through Simulated Data and Contextual Analysis for Building Extraction
by Frédéric Leroux, Mickaël Germain, Étienne Clabaut, Yacine Bouroubi and Tony St-Pierre
ISPRS Int. J. Geo-Inf. 2024, 13(1), 20; https://doi.org/10.3390/ijgi13010020 - 7 Jan 2024
Cited by 2 | Viewed by 3843
Abstract
Digital twins are increasingly gaining popularity as a method for simulating intricate natural and urban environments, with the precise segmentation of 3D objects playing an important role. This study focuses on developing a methodology for extracting buildings from textured 3D meshes, employing the [...] Read more.
Digital twins are increasingly gaining popularity as a method for simulating intricate natural and urban environments, with the precise segmentation of 3D objects playing an important role. This study focuses on developing a methodology for extracting buildings from textured 3D meshes, employing the PicassoNet-II semantic segmentation architecture. Additionally, we integrate Markov field-based contextual analysis for post-segmentation assessment and cluster analysis algorithms for building instantiation. Training a model to adapt to diverse datasets necessitates a substantial volume of annotated data, encompassing both real data from Quebec City, Canada, and simulated data from Evermotion and Unreal Engine. The experimental results indicate that incorporating simulated data improves segmentation accuracy, especially for under-represented features, and the DBSCAN algorithm proves effective in extracting isolated buildings. We further show that the model is highly sensible for the method of creating 3D meshes. Full article
(This article belongs to the Special Issue Unlocking the Power of Geospatial Data: Semantic Information Extraction, Ontology Engineering, and Deep Learning for Knowledge Discovery)
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21 pages, 6474 KB  
Article
Redesigning Graphical User Interface of Open-Source Geospatial Software in a Community-Driven Way: A Case Study of GRASS GIS
by Linda Karlovska, Anna Petrasova, Vaclav Petras and Martin Landa
ISPRS Int. J. Geo-Inf. 2023, 12(9), 376; https://doi.org/10.3390/ijgi12090376 - 10 Sep 2023
Cited by 1 | Viewed by 3470
Abstract
Learning to use geographic information system (GIS) software effectively may be intimidating due to the extensive range of features it offers. The GRASS GIS software, in particular, presents additional challenges for first-time users in terms of its complex startup procedure and unique terminology [...] Read more.
Learning to use geographic information system (GIS) software effectively may be intimidating due to the extensive range of features it offers. The GRASS GIS software, in particular, presents additional challenges for first-time users in terms of its complex startup procedure and unique terminology associated with its data structure. On the other hand, a substantial part of the GRASS user community including us as developers recognized and embraced the advantages of the current approach. Given the controversial nature of the whole issue, we decided to actively involve regular users by conducting several formal surveys and by performing usability testing. Throughout this process, we discovered that resolving specific software issues through pure user-centered design is not always feasible, particularly in the context of open-source scientific software where the boundary between users and developers is very fuzzy. To address this challenge, we adopted the user-centered methodology tailored to the requirements of open-source scientific software development, which we refer to as community-driven design. This paper describes the community-driven redesigning process on the GRASS GIS case study and sets a foundation for applying community-driven design in other open-source scientific projects by providing insights into effective software development practices driven by the needs and input of the project’s community. Full article
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15 pages, 8343 KB  
Article
The Impacts of Public Schools on Housing Prices of Residential Properties: A Case Study of Greater Sydney, Australia
by Yi Lu, Vivien Shi and Christopher James Pettit
ISPRS Int. J. Geo-Inf. 2023, 12(7), 298; https://doi.org/10.3390/ijgi12070298 - 24 Jul 2023
Cited by 5 | Viewed by 5198
Abstract
Residential property values are influenced by a combination of physical, socio-economic and neighbourhood factors. This study investigated the influence of public schools on residential property prices. Relatively few existing models have taken the spatial heterogeneity of different submarkets into account. To fill this [...] Read more.
Residential property values are influenced by a combination of physical, socio-economic and neighbourhood factors. This study investigated the influence of public schools on residential property prices. Relatively few existing models have taken the spatial heterogeneity of different submarkets into account. To fill this gap, three types of valuation models were applied to sales data from both non-strata and strata properties, and how the proximity and quality of public schools have influenced the prices of different residential property types was examined. The findings demonstrate that an increase of one unit in the normalised NAPLAN score of primary and high schools will lead to a 3.9% and 1.4%, 2.7% and 2.8% rise in housing prices for non-strata and strata properties, respectively. It is also indicated that the application of geographically weighted regression (GWR) can better capture the varying effects of schools across space. Moreover, properties located in the catchment of high-scoring schools in northern Greater Sydney are consistently the most influenced by school quality, regardless of the property type. These findings contribute to a comprehensive understanding of the relationships between public schools and the various submarkets of Greater Sydney. This is valuable for the decision-making processes of home buyers, developers and policymakers. Full article
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13 pages, 5732 KB  
Article
Mapping with ChatGPT
by Ran Tao and Jinwen Xu
ISPRS Int. J. Geo-Inf. 2023, 12(7), 284; https://doi.org/10.3390/ijgi12070284 - 16 Jul 2023
Cited by 54 | Viewed by 24353
Abstract
The emergence and rapid advancement of large language models (LLMs), represented by OpenAI’s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies are transforming the way we interact with information, communicate, and solve complex problems. [...] Read more.
The emergence and rapid advancement of large language models (LLMs), represented by OpenAI’s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies are transforming the way we interact with information, communicate, and solve complex problems. We conducted a pilot study exploring making maps with ChatGPT, a popular artificial intelligence (AI) chatbot. Specifically, we tested designing thematic maps using given or public geospatial data, as well as creating mental maps purely using textual descriptions of geographic space. We conclude that ChatGPT provides a useful alternative solution for mapping given its unique advantages, such as lowering the barrier to producing maps, boosting the efficiency of massive map production, and understanding geographical space with its spatial thinking capability. However, mapping with ChatGPT still has limitations at the current stage, such as its unequal benefits for different users and dependence on user intervention for quality control. Full article
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11 pages, 2037 KB  
Article
Exploring Spatial Mismatch between Primary Care and Older Populations in an Aging Country: A Case Study of South Korea
by Jeon-Young Kang, Sandy Wong, Jinwoo Park, Jinhyung Lee and Jared Aldstadt
ISPRS Int. J. Geo-Inf. 2023, 12(7), 255; https://doi.org/10.3390/ijgi12070255 - 22 Jun 2023
Cited by 5 | Viewed by 4301
Abstract
With the rapid growth of aging populations in South Korea, it is important to assess spatial accessibility to healthcare resources as older adults may need frequent visits to hospitals. Healthcare spatial accessibility is measured based on available resources (e.g., physicians, beds, services), demands [...] Read more.
With the rapid growth of aging populations in South Korea, it is important to assess spatial accessibility to healthcare resources as older adults may need frequent visits to hospitals. Healthcare spatial accessibility is measured based on available resources (e.g., physicians, beds, services), demands (e.g., population), and travel costs (e.g., distance or time). In this study, we employed an Enhanced Two-Step Floating Catchment Area (E2SFCA) method to measure the spatial accessibility to primary care for older populations (i.e., aged 65 and older) in major cities in South Korea, including Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan. We found that the aging population in Seoul, the capital and biggest city in South Korea, has relatively better accessibility than those living in other cities. We also discovered a negative relationship between accessibility to primary care and the aging index (i.e., population over 65 years old/population less than 15 years old); the regions with a higher ratio of older populations have lower accessibility to primary care. The results suggested that more primary care services (perhaps via mobile vans) are needed in regions predominantly with older people to improve their healthcare access. Full article
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22 pages, 3043 KB  
Article
PMGCN: Progressive Multi-Graph Convolutional Network for Traffic Forecasting
by Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen
ISPRS Int. J. Geo-Inf. 2023, 12(6), 241; https://doi.org/10.3390/ijgi12060241 - 16 Jun 2023
Cited by 6 | Viewed by 2996
Abstract
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is [...] Read more.
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph convolutional network (PMGCN), which includes spatiotemporal attention, multi-graph convolution, and multi-scale convolution modules. Specifically, we use a new spatiotemporal attention multi-graph convolution that can extract extensive and comprehensive dynamic spatial dependence between nodes, in which multiple graph convolutions adopt progressive connections and spatiotemporal attention dynamically adjusts each item of the Chebyshev polynomial in graph convolutions. In addition, multi-scale time convolution was added to obtain an extensive and comprehensive dynamic time dependence from multiple receptive field features. We used real datasets to predict traffic speed and traffic flow, and the results were compared with a variety of typical prediction models. PMGCN has the smallest Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) results under different horizons (H = 15 min, 30 min, 60 min), which shows the superiority of the proposed model. Full article
(This article belongs to the Special Issue Innovative GIS Models and Approaches for Large Environmental and Urban Applications in the Age of AI)
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17 pages, 5740 KB  
Article
A Dynamic Management and Integration Framework for Models in Landslide Early Warning System
by Liang Liu, Jiqiu Deng and Yu Tang
ISPRS Int. J. Geo-Inf. 2023, 12(5), 198; https://doi.org/10.3390/ijgi12050198 - 13 May 2023
Cited by 3 | Viewed by 3006
Abstract
The landslide early warning system (LEWS) relies on various models for data processing, prediction, forecasting, and warning level discrimination. The potential different programming implementations and dependencies of these models complicate the deployment and integration of LEWS. Moreover, the coupling between LEWS and models [...] Read more.
The landslide early warning system (LEWS) relies on various models for data processing, prediction, forecasting, and warning level discrimination. The potential different programming implementations and dependencies of these models complicate the deployment and integration of LEWS. Moreover, the coupling between LEWS and models makes it hard to modify or replace models rapidly and dynamically according to changes in business requirements (such as updating the early warning business process, adjusting the model parameters, etc.). This paper proposes a framework for dynamic management and integration of models in LEWS by using WebAPIs and Docker to standardize model interfaces and facilitate model deployment, using Kubernetes and Istio to enable microservice architecture, dynamic scaling, and high availability of models, and using a model repository management system to manage and orchestrate model-related information and application processes. The results of applying this framework to a real LEWS demonstrate that our approach can support efficient deployment, management, and integration of models within the system. Furthermore, it provides a rapid and feasible implementation method for upgrading, expanding, and maintaining LEWS in response to changes in business requirements. Full article
(This article belongs to the Topic Advances in Earth Observation and Geosciences)
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20 pages, 8792 KB  
Article
Dominant Modes of Agricultural Production Helped Structure Initial COVID-19 Spread in the U.S. Midwest
by Luke Bergmann, Luis Fernando Chaves, David O’Sullivan and Robert G. Wallace
ISPRS Int. J. Geo-Inf. 2023, 12(5), 195; https://doi.org/10.3390/ijgi12050195 - 9 May 2023
Cited by 5 | Viewed by 5377
Abstract
The spread of COVID-19 is geographically uneven in agricultural regions. Explanations proposed include differences in occupational risks, access to healthcare, racial inequalities, and approaches to public health. Here, we additionally explore the impacts of coexisting modes of agricultural production across counties from twelve [...] Read more.
The spread of COVID-19 is geographically uneven in agricultural regions. Explanations proposed include differences in occupational risks, access to healthcare, racial inequalities, and approaches to public health. Here, we additionally explore the impacts of coexisting modes of agricultural production across counties from twelve midwestern U.S. states. In modeling COVID-19 spread before vaccine authorization, we employed and extended spatial statistical methods that make different assumptions about the natures and scales of underlying sociospatial processes. In the process, we also develop a novel approach to visualizing the results of geographically weighted regressions that allows us to identify distinctive regional regimes of epidemiological processes. Our approaches allowed for models using abstract spatial weights (e.g., inverse-squared distances) to be meaningfully improved by also integrating process-specific relations (e.g., the geographical relations of the food system or of commuting). We thus contribute in several ways to methods in health geography and epidemiology for identifying contextually sensitive public engagements in socio-eco-epidemiological issues. Our results further show that agricultural modes of production are associated with the spread of COVID-19, with counties more engaged in modes of regenerative agricultural production having lower COVID-19 rates than those dominated by modes of conventional agricultural production, even when accounting for other factors. Full article
(This article belongs to the Collection Spatial Components of COVID-19 Pandemic)
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23 pages, 11603 KB  
Article
Assessment of Perceived and Physical Walkability Using Street View Images and Deep Learning Technology
by Youngok Kang, Jiyeon Kim, Jiyoung Park and Jiyoon Lee
ISPRS Int. J. Geo-Inf. 2023, 12(5), 186; https://doi.org/10.3390/ijgi12050186 - 2 May 2023
Cited by 34 | Viewed by 9212
Abstract
As neighborhood walkability has gradually become an important topic in various fields, many cities around the world are promoting an eco-friendly and people-centered walking environment as a top priority in urban planning. The purpose of this study is to visualize physical and perceived [...] Read more.
As neighborhood walkability has gradually become an important topic in various fields, many cities around the world are promoting an eco-friendly and people-centered walking environment as a top priority in urban planning. The purpose of this study is to visualize physical and perceived walkability in detail and analyze the differences to prepare alternatives for improving the neighborhood’s walking environment. The study area is Jeonju City, one of the medium-sized cities in Korea. For the evaluation of perceived walkability, 196,624 street view images were crawled and 127,317 pairs of training datasets were constructed. After developing a convolutional neural network model, the scores of perceived walkability are predicted. For the evaluation of physical walkability, eight indicators are selected, and the score of overall physical walkability is calculated by combining the scores of the eight indicators. After that, the scores of perceived and physical walkability are visualized, and the difference between them is analyzed. This study is novel in three aspects. First, we develop a deep learning model that can improve the accuracy of perceived walkability using street view images, even in small and medium-sized cities. Second, in analyzing the characteristics of street view images, the possibilities and limitations of the semantic segmentation technique are confirmed. Third, the differences between perceived and physical walkability are analyzed in detail, and how the results of our study can be used to prepare alternatives for improving the walking environment is presented. Full article
(This article belongs to the Special Issue Urban Geospatial Analytics Based on Crowdsourced Data)
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21 pages, 10571 KB  
Article
MAC-GAN: A Community Road Generation Model Combining Building Footprints and Pedestrian Trajectories
by Lin Yang, Jing Wei, Zejun Zuo and Shunping Zhou
ISPRS Int. J. Geo-Inf. 2023, 12(5), 181; https://doi.org/10.3390/ijgi12050181 - 25 Apr 2023
Cited by 3 | Viewed by 2898
Abstract
Community roads are crucial to community navigation. There are automatic methods to obtain community roads using trajectories, but the sparsity and uneven density distribution of community trajectories present significant challenges in identifying community roads. To overcome these challenges, we propose a conditional generative [...] Read more.
Community roads are crucial to community navigation. There are automatic methods to obtain community roads using trajectories, but the sparsity and uneven density distribution of community trajectories present significant challenges in identifying community roads. To overcome these challenges, we propose a conditional generative adversarial network (MAC-GAN) supervised by pedestrian trajectories and neighborhood building footprints for road generation. MAC-GAN packs the “road trajectory–building footprint” pairs into images to characterize implicit ternary relations and sets up a multi-scale skip-connected and asymmetric convolution-based generator to incorporate such a relationship, in which the generator and discriminator mutually learn to optimize the network parameters and then derive approximate optimal results. Experiments on 37 real-world community datasets in Wuhan, China, are conducted to verify the effectiveness of the proposed model. The experimental results show that the F1 score of our model increases by 1.7–6.8%, and the IOU of our model increases by 2.2–7.5% compared with three baselines (i.e., Pix2pix, GANmapper, and DLinkGAN (configured by DLinknet)). In areas with sparse and missing trajectory data, the generated fine roads have high accuracy with the supervision of building footprints. Full article
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